Apparatuses and methods for revealing user identifiers on an immutable sequential listing

ABSTRACT

An exemplary apparatus includes a processor and a memory communicatively connected to the processor, the memory containing instructions configuring the processor to store, using a computing device, on an immutable sequential listing, a plurality of user identifiers, wherein each user identifier of the plurality of user identifiers is associated with the same user, each user identifier of the plurality of user identifiers is associated with a plurality of action data, and each of the plurality of user identifiers is associated with a user role of the user, receive, using a computing device, information relating to an element of posting data associated with a posting generator, classify, as a function of the received information, the information to a user identifier of the plurality of user identifiers as a function of the plurality of action data associated with the user identifier and reveal the user identifier to the posting generator.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of Non-provisional Application No.17/667,495, filed on Feb. 8, 2022, and entitled “APPARATUSES AND METHODSFOR REVEALING USER IDENTIFIERS ON AN IMMUTABLE SEQUENTIAL LISTING,” theentirety of which is incorporated herein by reference.

FIELD OF THE INVENTION

The present invention generally relates to the field of human resourcetechnology. In particular, the present invention is directed toapparatuses and methods for revealing user identifiers on an immutablesequential listing to a posting generator.

BACKGROUND

Immutable sequential listings are a wonderful way to securely upload andstore sensitive information. However, a problem arises when it comes toefficiently revealing relevant information on the immutable sequentiallisting.

SUMMARY OF THE DISCLOSURE

In an aspect, an apparatus for revealing user identifiers on animmutable sequential listing to a posting generator is illustrated. Theapparatus includes at least a processor, and a memory communicativelyconnected to the processor, the memory containing instructionsconfiguring the at least a processor to store on an immutable sequentiallisting, a plurality of user identifiers, wherein each user identifierof the plurality of user identifiers is associated with the same user,each user identifier of the plurality of user identifiers is associatedwith a plurality of action data, and each user identifier of theplurality of user identifiers is associated with a user role of theuser, receive information relating to an element of posting dataassociated with a posting generator, classify the received informationto a user identifier of the plurality of user identifiers as a functionof the plurality of action data associated with the user identifier,wherein classifying the received information to the user identifierfurther includes training a machine learning algorithm with trainingdata to generate a first machine learning model, wherein the trainingdata correlates posting information data to user action data, andclassifying the received information to the user identifier using thefirst machine learning model, and reveal the user identifier to theposting generator.

In another aspect, a method for revealing user identifiers on animmutable sequential listing to a posting generator, the methodincluding storing, using a computing device, on an immutable sequentiallisting, a plurality of user identifiers, wherein each user identifierof the plurality of user identifiers is associated with the same user,each user identifier of the plurality of user identifiers is associatedwith a plurality of action data, and each user identifier of theplurality of user identifiers is associated with a user role of theuser, receiving, using the computing device, information relating to anelement of posting data associated with a posting generator,classifying, using the computing device, the information to a useridentifier of the plurality of user identifiers as a function of theplurality of action data associated with the user identifier, whereinclassifying the received information to the user identifier furtherincludes training a machine learning algorithm with training data togenerate a first machine learning model, wherein the training datacorrelates posting information data to user action data, and classifyingthe received information to the user identifier using the first machinelearning model, and revealing, using a computing device, the useridentifier to the posting generator.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram of an embodiment of an apparatus for revealinguser identifiers on an immutable sequential listing to a postinggenerator;

FIG. 2 is a block diagram of exemplary embodiment of an immutablesequential listing;

FIG. 3 is a block diagram of exemplary embodiment of a machine learningmodule;

FIG. 4 illustrates an exemplary nodal network;

FIG. 5 is a block diagram of an exemplary node;

FIG. 6 is a graph illustrating an exemplary relationship between fuzzysets;

FIG. 7 is a flow diagram of an exemplary method for revealing useridentifiers on an immutable sequential listing to a posting generator;and

FIG. 8 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed toapparatuses and methods for revealing user identifiers on an immutablesequential listing to a posting generator. In an embodiment, a postinggenerator may be a hiring entity.

Aspects of the present disclosure can be utilized by job recruitersand/or staffing agencies to know which jobseeker profiles are the mostrelevant for employment consideration.

Exemplary embodiments illustrating aspects of the present disclosure aredescribed below in the context of several specific examples.

In an embodiment, methods and apparatuses described herein may performor implement one or more aspects of a cryptographic system. In oneembodiment, a cryptographic system is a system that converts data from afirst form, known as “plaintext,” which is intelligible when viewed inits intended format, into a second form, known as “ciphertext,” which isnot intelligible when viewed in the same way. Ciphertext may beunintelligible in any format unless first converted back to plaintext.In one embodiment, a process of converting plaintext into ciphertext isknown as “encryption.” Encryption process may involve the use of adatum, known as an “encryption key,” to alter plaintext. Cryptographicsystem may also convert ciphertext back into plaintext, which is aprocess known as “decryption.” Decryption process may involve the use ofa datum, known as a “decryption key,” to return the ciphertext to itsoriginal plaintext form. In embodiments of cryptographic systems thatare “symmetric,” decryption key is essentially the same as encryptionkey: possession of either key makes it possible to deduce the other keyquickly without further secret knowledge. Encryption and decryption keysin symmetric cryptographic systems may be kept secret and shared onlywith persons or entities that the user of the cryptographic systemwishes to be able to decrypt the ciphertext. One example of a symmetriccryptographic system is the Advanced Encryption Standard (“AES”), whicharranges plaintext into matrices and then modifies the matrices throughrepeated permutations and arithmetic operations with an encryption key.

In embodiments of cryptographic systems that are “asymmetric,” eitherencryption or decryption key cannot be readily deduced withoutadditional secret knowledge, even given the possession of acorresponding decryption or encryption key, respectively; a commonexample is a “public key cryptographic system,” in which possession ofthe encryption key does not make it practically feasible to deduce thedecryption key, so that the encryption key may safely be made availableto the public. An example of a public key cryptographic system is RSA,in which an encryption key involves the use of numbers that are productsof very large prime numbers, but a decryption key involves the use ofthose very large prime numbers, such that deducing the decryption keyfrom the encryption key requires the practically infeasible task ofcomputing the prime factors of a number which is the product of two verylarge prime numbers. Another example is elliptic curve cryptography,which relies on the fact that given two points P and Q on an ellipticcurve over a finite field, and a definition for addition where A + B =-R, the point where a line connecting point A and point B intersects theelliptic curve, where “0,” the identity, is a point at infinity in aprojective plane containing the elliptic curve, finding a number k suchthat adding P to itself k times results in Q is computationallyimpractical, given correctly selected elliptic curve, finite field, andP and Q. A further example of asymmetrical cryptography may includelattice-based cryptography, which relies on the fact that variousproperties of sets of integer combination of basis vectors are hard tocompute, such as finding the one combination of basis vectors thatresults in the smallest Euclidean distance. Embodiments of cryptography,whether symmetrical or asymmetrical, may include quantum-securecryptography, defined for the purposes of this disclosure ascryptography that remains secure against adversaries possessing quantumcomputers; some forms of lattice-based cryptography, for instance, maybe quantum-secure.

In some embodiments, apparatuses and methods described herein producecryptographic hashes, also referred to by the equivalent shorthand term“hashes.” A cryptographic hash, as used herein, is a mathematicalrepresentation of a lot of data, such as files or blocks in a blockchain as described in further detail below; the mathematicalrepresentation is produced by a lossy “one-way” algorithm known as a“hashing algorithm.” Hashing algorithm may be a repeatable process; thatis, identical lots of data may produce identical hashes each time theyare subjected to a particular hashing algorithm. Because hashingalgorithm is a one-way function, it may be impossible to reconstruct alot of data from a hash produced from the lot of data using the hashingalgorithm. In the case of some hashing algorithms, reconstructing thefull lot of data from the corresponding hash using a partial set of datafrom the full lot of data may be possible only by repeatedly guessing atthe remaining data and repeating the hashing algorithm; it is thuscomputationally difficult if not infeasible for a single computer toproduce the lot of data, as the statistical likelihood of correctlyguessing the missing data may be extremely low. However, the statisticallikelihood of a computer of a set of computers simultaneously attemptingto guess the missing data within a useful timeframe may be higher,permitting mining protocols as described in further detail below.

In an embodiment, hashing algorithm may demonstrate an “avalancheeffect,” whereby even extremely small changes to lot of data producedrastically different hashes. This may thwart attempts to avoid thecomputational work necessary to recreate a hash by simply inserting afraudulent datum in data lot, enabling the use of hashing algorithms for“tamper-proofing” data such as data contained in an immutable ledger asdescribed in further detail below. This avalanche or “cascade” effectmay be evinced by various hashing processes; persons skilled in the art,upon reading the entirety of this disclosure, will be aware of varioussuitable hashing algorithms for purposes described herein. Verificationof a hash corresponding to a lot of data may be performed by running thelot of data through a hashing algorithm used to produce the hash. Suchverification may be computationally expensive, albeit feasible,potentially adding up to significant processing delays where repeatedhashing, or hashing of large quantities of data, is required, forinstance as described in further detail below. Examples of hashingprograms include, without limitation, SHA256, a NIST standard; furthercurrent and past hashing algorithms include Winternitz hashingalgorithms, various generations of Secure Hash Algorithm (including“SHA-1,” “SHA-2,” and “SHA-3”), “Message Digest” family hashes such as“MD4,” “MD5,” “MD6,” and “RIPEMD,” Keccak, “BLAKE” hashes and progeny(e.g., “BLAKE2,” “BLAKE-256,” “BLAKE-512,” and the like), MessageAuthentication Code (“MAC”)-family hash functions such as PMAC, OMAC,VMAC, HMAC, and UMAC, Poly1305-AES, Elliptic Curve Only Hash (“ECOH”)and similar hash functions, Fast-Syndrome-based (FSB) hash functions,GOST hash functions, the Grøstl hash function, the HAS-160 hashfunction, the JH hash function, the RadioGatún hash function, the Skeinhash function, the Streebog hash function, the SWIFFT hash function, theTiger hash function, the Whirlpool hash function, or any hash functionthat satisfies, at the time of implementation, the requirements that acryptographic hash be deterministic, infeasible to reverse-hash,infeasible to find collisions, and have the property that small changesto an original message to be hashed will change the resulting hash soextensively that the original hash and the new hash appear uncorrelatedto each other. A degree of security of a hash function in practice maydepend both on the hash function itself and on characteristics of themessage and/or digest used in the hash function. For example, where amessage is random, for a hash function that fulfillscollision-resistance requirements, a brute-force or “birthday attack”may to detect collision may be on the order of O(2n/2) for n outputbits; thus, it may take on the order of 2256 operations to locate acollision in a 512 bit output “Dictionary” attacks on hashes likely tohave been generated from a non-random original text can have a lowercomputational complexity, because the space of entries they are guessingis far smaller than the space containing all random permutations ofbits. However, the space of possible messages may be augmented byincreasing the length or potential length of a possible message, or byimplementing a protocol whereby one or more randomly selected strings orsets of data are added to the message, rendering a dictionary attacksignificantly less effective.

In some embodiments, apparatuses and methods described herein maygenerate, evaluate, and/or utilize digital signatures. A “digitalsignature,” as used herein, includes a secure proof of possession of asecret by a signing device, as performed on provided element of data,known as a “message.” A message may include an encrypted mathematicalrepresentation of a file or other set of data using the private key of apublic key cryptographic system. Secure proof may include any form ofsecure proof as described above, including without limitation encryptionusing a private key of a public key cryptographic system as describedabove. Signature may be verified using a verification datum suitable forverification of a secure proof; for instance, where secure proof isenacted by encrypting message using a private key of a public keycryptographic system, verification may include decrypting the encryptedmessage using the corresponding public key and comparing the decryptedrepresentation to a purported match that was not encrypted; if thesignature protocol is well-designed and implemented correctly, thismeans the ability to create the digital signature is equivalent topossession of the private decryption key and/or device-specific secret.Likewise, if a message making up a mathematical representation of fileis well-designed and implemented correctly, any alteration of the filemay result in a mismatch with the digital signature; the mathematicalrepresentation may be produced using an alteration-sensitive, reliablyreproducible algorithm, such as a hashing algorithm as described above.A mathematical representation to which the signature may be compared maybe included with signature, for verification purposes; in otherembodiments, the algorithm used to produce the mathematicalrepresentation may be publicly available, permitting the easyreproduction of the mathematical representation corresponding to anyfile.

In some embodiments, digital signatures may be combined with orincorporated in digital certificates. In one embodiment, a digitalcertificate is a file that conveys information and links the conveyedinformation to a “certificate authority” that is the issuer of a publickey in a public key cryptographic system. Certificate authority in someembodiments contains data conveying the certificate authority’sauthorization for the recipient to perform a task. The authorization maybe the authorization to access a given datum. The authorization may bethe authorization to access a given process. In some embodiments, thecertificate may identify the certificate authority. The digitalcertificate may include a digital signature. A third party such as acertificate authority (CA) is available to verify that the possessor ofthe private key is a particular entity; thus, if the certificateauthority may be trusted, and the private key has not been stolen, theability of an entity to produce a digital signature confirms theidentity of the entity and links the file to the entity in a verifiableway. Digital signature may be incorporated in a digital certificate,which is a document authenticating the entity possessing the private keyby authority of the issuing certificate authority and signed with adigital signature created with that private key and a mathematicalrepresentation of the remainder of the certificate. In otherembodiments, digital signature is verified by comparing the digitalsignature to one known to have been created by the entity thatpurportedly signed the digital signature; for instance, if the publickey that decrypts the known signature also decrypts the digitalsignature, the digital signature may be considered verified. Digitalsignature may also be used to verify that the file has not been alteredsince the formation of the digital signature.

Referring now to FIG. 1 , an exemplary embodiment of apparatus 100 forrevealing user identifiers 124 on immutable sequential listing 120 to aposting generator is illustrated. Apparatus 100 includes processor 104and memory 108 communicatively connected to processor 104, whereinmemory 108 contains instructions configuring processor 104 to carry outthe revealing process. Apparatus 100 includes computing device 112.Apparatus 100 is communicatively connected to computing device 112. Asused in this disclosure, “communicatively connected” means connected byway of a connection, attachment or linkage between two or more relatawhich allows for reception and/or transmittance of informationtherebetween. For example, and without limitation, this connection maybe wired or wireless, direct or indirect, and between two or morecomponents, circuits, devices, systems, and the like, which allows forreception and/or transmittance of data and/or signal(s) therebetween.Data and/or signals therebetween may include, without limitation,electrical, electromagnetic, magnetic, video, audio, radio and microwavedata and/or signals, combinations thereof, and the like, among others. Acommunicative connection may be achieved, for example and withoutlimitation, through wired or wireless electronic, digital or analog,communication, either directly or by way of one or more interveningdevices or components. Further, communicative connection may includeelectrically coupling or connecting at least an output of one device,component, or circuit to at least an input of another device, component,or circuit. For example, and without limitation, via a bus or otherfacility for intercommunication between elements of a computing device.Communicative connecting may also include indirect connections via, forexample and without limitation, wireless connection, radiocommunication, low power wide area network, optical communication,magnetic, capacitive, or optical coupling, and the like. In someinstances, the terminology “communicatively coupled” may be used inplace of communicatively connected in this disclosure. Computing device112 may include any computing device as described in this disclosure,including without limitation a microcontroller, microprocessor, digitalsignal processor (DSP) and/or system on a chip (SoC) as described inthis disclosure. Computing device 112 may include, be included in,and/or communicate with a mobile device such as a mobile telephone orsmartphone. Computing device 112 may include a single computing deviceoperating independently, or may include two or more computing deviceoperating in concert, in parallel, sequentially or the like; two or morecomputing devices may be included together in a single computing deviceor in two or more computing devices. Computing device 112 may interfaceor communicate with one or more additional devices as described below infurther detail via a network interface device. Network interface devicemay be utilized for connecting computing device 112 to one or more of avariety of networks, and one or more devices. Examples of a networkinterface device include, but are not limited to, a network interfacecard (e.g., a mobile network interface card, a LAN card), a modem, andany combination thereof. Examples of a network include, but are notlimited to, a wide area network (e.g., the Internet, an enterprisenetwork), a local area network (e.g., a network associated with anoffice, a building, a campus or other relatively small geographicspace), a telephone network, a data network associated with atelephone/voice provider (e.g., a mobile communications provider dataand/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network may employ a wiredand/or a wireless mode of communication. In general, any networktopology may be used. Information (e.g., data, software etc.) may becommunicated to and/or from a computer and/or a computing device.Computing device 112 may include but is not limited to, for example, acomputing device or cluster of computing devices in a first location anda second computing device or cluster of computing devices in a secondlocation. Computing device 112 may include one or more computing devicesdedicated to data storage, security, distribution of traffic for loadbalancing, and the like. Computing device 112 may distribute one or morecomputing tasks as described below across a plurality of computingdevices of computing device, which may operate in parallel, in series,redundantly, or in any other manner used for distribution of tasks ormemory between computing devices. Computing device 112 may beimplemented using a “shared nothing” architecture in which data iscached at the worker, in an embodiment, this may enable scalability ofapparatus 100 and/or computing device 112.

With continued reference to FIG. 1 , processor 104 and/or computingdevice 112 may be designed and/or configured by memory 108 to performany method, method step, or sequence of method steps in any embodimentdescribed in this disclosure, in any order and with any degree ofrepetition. For instance, processor 104 and/or computing device 112 maybe configured to perform a single step or sequence repeatedly until adesired or commanded outcome is achieved; repetition of a step or asequence of steps may be performed iteratively and/or recursively usingoutputs of previous repetitions as inputs to subsequent repetitions,aggregating inputs and/or outputs of repetitions to produce an aggregateresult, reduction or decrement of one or more variables such as globalvariables, and/or division of a larger processing task into a set ofiteratively addressed smaller processing tasks. Processor 104 and/orcomputing device 112 may perform any step or sequence of steps asdescribed in this disclosure in parallel, such as simultaneously and/orsubstantially simultaneously performing a step two or more times usingtwo or more parallel threads, processor cores, or the like; division oftasks between parallel threads and/or processes may be performedaccording to any protocol suitable for division of tasks betweeniterations. Persons skilled in the art, upon reviewing the entirety ofthis disclosure, will be aware of various ways in which steps, sequencesof steps, processing tasks, and/or data may be subdivided, shared, orotherwise dealt with using iteration, recursion, and/or parallelprocessing. Processor 104 and/or computing device 112 may performdeterminations, classification, and/or analysis steps, methods,processes, or the like as described in this disclosure using machinelearning processes 116. A “machine learning process,” as used in thisdisclosure, is a process that automatedly uses a body of data known as“training data” and/or a “training set” (described further below) togenerate an algorithm that will be performed by a computingdevice/module to produce outputs given data provided as inputs; this isin contrast to a non-machine learning software program where thecommands to be executed are determined in advance by a user and writtenin a programming language.

Processor 104 and/or computing device 112 may perform determinations,classification, and/or analysis steps, methods, processes, or the likeas described in this disclosure using machine learning processes 116. A“machine learning process,” as used in this disclosure, is a processthat automatedly uses a body of data known as “training data” and/or a“training set” (described further below) to generate an algorithm thatwill be performed by a computing device/module to produce outputs givendata provided as inputs; this is in contrast to a non-machine learningsoftware program where the commands to be executed are determined inadvance by a user and written in a programming language.

With continued reference to FIG. 1 , processor 104 and/or computingdevice 112 is configured to reveal user identifiers 124 on immutablesequential listing 120 to a posting generator. As used in thisdisclosure, a “user identifier” is an immutable sequential listingidentifier which is an element of data used to identify a user; a usermay be associated with a plurality of user identifiers. A user mayinclude someone seeking to enhance or advance their professional, workand/or personal life. For example, a user may be a job-seeking applicantor an opportunity-monitoring person or entity. In some embodiments, useridentifier 124 could be addresses in immutable sequential listing 120.As used in this disclosure, a “posting generator” is a hiring entity oremployer. For example, the posting generator could be a company, jobmatching service, and the like. Processor 104 and/or computing device112 is configured to store on immutable sequential listing 120, aplurality of user identifiers 124, wherein each user identifier 124 ofthe plurality of user identifiers 124 is associated with the same user.Each user identifier 124 of the plurality of user identifiers 124 isalso associated with a plurality of action data 128, and each of theplurality of user identifiers 124 is associated with user role 132 ofthe user. As used in this disclosure, “action data” is any informationon a user pertaining to areas such as professional, work, educationaland/or personal accomplishments, qualifications, interests, and thelike. As used in this disclosure, a “user role” is a title, credential,position, or an affiliation a user may have in a field. Each useridentifier 124 may be associated with a different user role 132 whichthe user may perform. For example, user may have two or moreprofessional affiliations such as in STEM and patent law, two or moreeducational affiliations such as teaching and studying, two or morecommunity affiliations such as in healthcare and coaching sports, andthe like, among others. User identifiers 124 may be based on each ofsuch user roles 132. User identifiers 124 may also be based on differentsectors of a user’s life, for example, an identifier for education, anidentifier for personal life, an identifier for work history, and thelike, among others.

Still referring to FIG. 1 , processor 104 and/or computing device 112 isconfigured to receive information relating to an element of posting data136 associated with a posting generator. As used in this disclosure,“posting data” is information pertaining to the requirements,descriptions, preferences, and the like of a posting generator. In someembodiments, posting data may be stored in a database connected tocomputing device 112 using any network interface described throughoutthis disclosure. As used in this disclosure, a “posting database” isdatabase containing a plurality of posting data 136 from numerous or thesame posting generator. For example a posting generator may uploaddocuments containing the user requirements for a job to a postingdatabase 140. Processor 104 and/or computing device 112 may then accessthose documents in the database through a network to download a documentand parse elements of posting data using a language processor module. Alanguage processing module may include any hardware and/or softwaremodule. The language processing module may be configured to extract,from the one or more documents, one or more words. One or more words mayinclude, without limitation, strings of one or more characters,including without limitation any sequence or sequences of letters,numbers, punctuation, diacritic marks, engineering symbols, geometricdimensioning and tolerancing (GD&T) symbols, chemical symbols andformulas, spaces, whitespace, and other symbols, including any symbolsusable as textual data as described above. Textual data may be parsedinto tokens, which may include a simple word (sequence of lettersseparated by whitespace) or more generally a sequence of characters asdescribed previously. The term “token,” as used herein, refers to anysmaller, individual groupings of text from a larger source of text;tokens may be broken up by word, pair of words, sentence, or otherdelimitation. These tokens may in turn be parsed in various ways.Textual data may be parsed into words or sequences of words, which maybe considered words as well. Textual data may be parsed into “n-grams”,where all sequences of n consecutive characters are considered. Any orall possible sequences of tokens or words may be stored as “chains”, forexample for use as a Markov chain or Hidden Markov Model, describedfurther below.

Still referring to FIG. 1 , processor 104 and/or computing device 112 isconfigured to classify, as a function of the received information, theinformation to a user identifier 124 of the plurality of useridentifiers 124 as a function of the plurality of action data 128associated with user identifier 124. In some embodiments classifying thereceived information may include a language processor module to matcheach term of posting data 136 to a plurality of synonyms of termscontained in action data 128 and user role 132 associated with useridentifier 124 of the plurality of user identifiers 124. This processmay be repeated for the total plurality of user identifiers 124 storedon immutable sequential listing 120. In some embodiments, classificationis used to recognize user identifiers 124 of the plurality of useridentifiers 124 that are relevant to a posting generator. In someembodiments, determining relevant user identifiers 124 may include alinguistic classifier, wherein information contained in posting data 136is an input, and the classifier additionally uses a language processingmodule to output a categorization of relevant user identifiers 124. Insome embodiments, data comparison of the posting generator and theplurality of user identifiers 124 may include comparing jobdescriptions, work experiences, education history, etc. For example,posting data 136 that contains a job description about electricalengineering may be a classifier input to generate a list of electricalengineer user identifiers 124 that textually match the job description.

Still referring to FIG. 1 , the language processing module may operateto produce a language processing model. Language processing model mayinclude a program automatically generated by computing device and/orlanguage processing module to produce associations between one or morewords extracted from at least a document and detect associations,including without limitation mathematical associations, between suchwords. For example, processor 104 and/or computing device 112 may take aterm form an element of posting data such as “Attorney” and match to aplurality of user identifiers 124 that contains synonyms such as“Advocate”, “Counsel”, “Lawyer”, and the like. Processor 104 and/orcomputing device 112 may follow this logic to match each term in postingdata 136 to any similar term to user identifier 124. Processor 104and/or computing device 112 may also classify a string of terms fromposting data 136 to user identifier 124. For example. Processor 104and/or computing device 112 may take “Charge Nurse, 5 years ofexperience” and classify it to “Licensed Practical Nurse at JohnHospital 2017 - 2022”. Associations between language elements, wherelanguage elements include for purposes herein extracted words,relationships of such categories to other such term may include, withoutlimitation, mathematical associations, including without limitationstatistical correlations between any language element and any otherlanguage element and/or language elements. Statistical correlationsand/or mathematical associations may include probabilistic formulas orrelationships indicating, for instance, a likelihood that a givenextracted word indicates a given category of semantic meaning. As afurther example, statistical correlations and/or mathematicalassociations may include probabilistic formulas or relationshipsindicating a positive and/or negative association between at least anextracted word and/or a given semantic meaning; positive or negativeindication may include an indication that a given document is or is notindicating a category semantic meaning. Whether a phrase, sentence,word, or other textual element in a document or corpus of documentsconstitutes a positive or negative indicator may be determined, in anembodiment, by mathematical associations between detected words,comparisons to phrases and/or words indicating positive and/or negativeindicators that are stored in memory at computing device, or the like.

Still referring to FIG. 1 , language processing module may generate thelanguage processing model by any suitable method, including withoutlimitation a natural language processing classification algorithm;language processing model may include a natural language processclassification model that enumerates and/or derives statisticalrelationships between input terms and output terms. Algorithm togenerate language processing model may include a stochastic gradientdescent algorithm, which may include a method that iteratively optimizesan objective function, such as an objective function representing astatistical estimation of relationships between terms, includingrelationships between input terms and output terms, in the form of a sumof relationships to be estimated. In an alternative or additionalapproach, sequential tokens may be modeled as chains, serving as theobservations in a Hidden Markov Model (HMM). HMMs as used herein arestatistical models with inference algorithms that that may be applied tothe models. In such models, a hidden state to be estimated may includean association between an extracted words, phrases, and/or othersemantic units. There may be a finite number of categories to which anextracted word may pertain; an HMM inference algorithm, such as theforward-backward algorithm or the Viterbi algorithm, may be used toestimate the most likely discrete state given a word or sequence ofwords. Language processing module may combine two or more approaches.For instance, and without limitation, machine-learning program may use acombination of Naive-Bayes (NB), Stochastic Gradient Descent (SGD), andparameter grid-searching classification techniques; the result mayinclude a classification algorithm that returns ranked associations.

Continuing to refer to FIG. 1 , generating language processing model mayinclude generating a vector space, which may be a collection of vectors,defined as a set of mathematical objects that can be added togetherunder an operation of addition following properties of associativity,commutativity, existence of an identity element, and existence of aninverse element for each vector, and can be multiplied by scalar valuesunder an operation of scalar multiplication compatible with fieldmultiplication, and that has an identity element is distributive withrespect to vector addition, and is distributive with respect to fieldaddition. Each vector in an n-dimensional vector space may berepresented by an n-tuple of numerical values. Each unique extractedword and/or language element as described above may be represented by avector of the vector space. In an embodiment, each unique extractedand/or other language element may be represented by a dimension ofvector space; as a non-limiting example, each element of a vector mayinclude a number representing an enumeration of co-occurrences of theword and/or language element represented by the vector with another wordand/or language element. Vectors may be normalized, scaled according torelative frequencies of appearance and/or file sizes. In an embodimentassociating language elements to one another as described above mayinclude computing a degree of vector similarity between a vectorrepresenting each language element and a vector representing anotherlanguage element; vector similarity may be measured according to anynorm for proximity and/or similarity of two vectors, including withoutlimitation cosine similarity, which measures the similarity of twovectors by evaluating the cosine of the angle between the vectors, whichcan be computed using a dot product of the two vectors divided by thelengths of the two vectors. Degree of similarity may include any othergeometric measure of distance between vectors.

Still referring to FIG. 1 , language processing module may use a corpusof documents to generate associations between language elements in alanguage processing module, and diagnostic engine may then use suchassociations to analyze words extracted from one or more documents anddetermine that the one or more documents indicate significance of acategory. In an embodiment, language module and/or computing device mayperform this analysis using a selected set of significant documents,such as documents identified by one or more experts as representing goodinformation; experts may identify or enter such documents via graphicaluser interface, or may communicate identities of significant documentsaccording to any other suitable method of electronic communication, orby providing such identity to other persons who may enter suchidentifications into computing device. Documents may be entered into acomputing device by being uploaded by an expert or other persons using,without limitation, file transfer protocol (FTP) or other suitablemethods for transmission and/or upload of documents; alternatively oradditionally, where a document is identified by a citation, a uniformresource identifier (URI), uniform resource locator (URL) or other datumpermitting unambiguous identification of the document, diagnostic enginemay automatically obtain the document using such an identifier, forinstance by submitting a request to a database or compendium ofdocuments such as JSTOR as provided by Ithaka Harbors, Inc. of New York.

Still referring to FIG. 1 , in some embodiments, classifying thereceived information may also include a machine learning classifier tomatch posting data 136 to a particular user identifier 124 of theplurality of user identifiers 124 as function of the plurality ofsynonyms and/or as a function of the action data. As used in thisdisclosure, a “classifier” is a machine-learning model, such as amathematical model, neural net, or program generated by a machinelearning algorithm known as a “classification algorithm,” as describedin further detail below, that sorts inputs into categories or bins ofdata, outputting the categories or bins of data and/or labels associatedtherewith. A classifier may be configured to output at least a datumthat labels or otherwise identifies a set of data that are clusteredtogether, found to be close under a distance metric as described below,or the like. For example, an address classifier may take posting data136 and the plurality of user identifiers 124 as inputs and outputdifferent bins of data, wherein in each bin of data is a specificmatching of the posting data to a particular user identifier 124 of theplurality of user identifiers 124. Classifier matching may be based ontraining data containing, any data sets described throughout thisdisclosure along with the output data from the linguistic classifierthat matched posting data terms to the plurality of user identifiers 124synonyms. Each bin of data may be labeled or numbered to distinguishfrom one another. In some embodiments, each data bin may be labeled withthe address of the user belonging to user identifier 124. For example,an address classification algorithm may take posting data related toelectrical engineering as an input and output bins of user identifieraddresses that relate to the posting data based on the output data ofthe linguistic classifier as described above.

Still referring to FIG. 1 , classification may be performed using,without limitation, linear classifiers such as without limitationlogistic regression and/or naive Bayes classifiers, nearest neighborclassifiers such as k-nearest neighbors classifiers, support vectormachines, least squares support vector machines, fisher’s lineardiscriminant, quadratic classifiers, decision trees, boosted trees,random forest classifiers, learning vector quantization, and/or neuralnetwork-based classifiers. Computing device 112 may be configured togenerate a classifier using a Naïve Bayes classification algorithm.Naïve Bayes classification algorithm generates classifiers by assigningclass labels to problem instances, represented as vectors of elementvalues. Class labels are drawn from a finite set. Naïve Bayesclassification algorithm may include generating a family of algorithmsthat assume that the value of a particular element is independent of thevalue of any other element, given a class variable. Naïve Bayesclassification algorithm may be based on Bayes Theorem expressed asP(A/B)= P(B/A) P(A)÷P(B), where P(A/B) is the probability of hypothesisA given data B also known as posterior probability; P(B/A) is theprobability of data B given that the hypothesis A was true; P(A) is theprobability of hypothesis A being true regardless of data also known asprior probability of A; and P(B) is the probability of the dataregardless of the hypothesis. A naïve Bayes algorithm may be generatedby first transforming training data into a frequency table. Computingdevice 112 may then calculate a likelihood table by calculatingprobabilities of different data entries and classification labels.Computing device 112 may utilize a naïve Bayes equation to calculate aposterior probability for each class. A class containing the highestposterior probability is the outcome of prediction. Naïve Bayesclassification algorithm may include a gaussian model that follows anormal distribution. Naïve Bayes classification algorithm may include amultinomial model that is used for discrete counts. Naïve Bayesclassification algorithm may include a Bernoulli model that may beutilized when vectors are binary.

With continued reference to FIG. 1 , computing device 112 may beconfigured to generate a classifier using a K-nearest neighbors (KNN)algorithm. A “K-nearest neighbors algorithm” as used in this disclosure,includes a classification method that utilizes feature similarity toanalyze how closely out-of-sample- features resemble training data toclassify input data to one or more clusters and/or categories offeatures as represented in training data; this may be performed byrepresenting both training data and input data in vector forms, andusing one or more measures of vector similarity to identifyclassifications within training data, and to determine a classificationof input data. K-nearest neighbors algorithm may include specifying aK-value, or a number directing the classifier to select the k mostsimilar entries training data to a given sample, determining the mostcommon classifier of the entries in the database, and classifying theknown sample; this may be performed recursively and/or iteratively togenerate a classifier that may be used to classify input data as furthersamples. For instance, an initial set of samples may be performed tocover an initial heuristic and/or “first guess” at an output and/orrelationship, which may be seeded, without limitation, using expertinput received according to any process as described herein. As anon-limiting example, an initial heuristic may include a ranking ofassociations between inputs and elements of training data. Heuristic mayinclude selecting some number of highest-ranking associations and/ortraining data elements.

With continued reference to FIG. 1 , generating k-nearest neighborsalgorithm may generate a first vector output containing a data entrycluster, generating a second vector output containing an input data, andcalculate the distance between the first vector output and the secondvector output using any suitable norm such as cosine similarity,Euclidean distance measurement, or the like. Each vector output may berepresented, without limitation, as an n-tuple of values, where n is atleast two values. Each value of n-tuple of values may represent ameasurement or other quantitative value associated with a given categoryof data, or attribute, examples of which are provided in further detailbelow; a vector may be represented, without limitation, in n-dimensionalspace using an axis per category of value represented in n-tuple ofvalues, such that a vector has a geometric direction characterizing therelative quantities of attributes in the n-tuple as compared to eachother. Two vectors may be considered equivalent where their directions,and/or the relative quantities of values within each vector as comparedto each other, are the same; thus, as a non-limiting example, a vectorrepresented as [5, 10, 15] may be treated as equivalent, for purposes ofthis disclosure, as a vector represented as [1, 2, 3]. Vectors may bemore similar where their directions are more similar, and more differentwhere their directions are more divergent; however, vector similaritymay alternatively or additionally be determined using averages ofsimilarities between like attributes, or any other measure of similaritysuitable for any n-tuple of values, or aggregation of numericalsimilarity measures for the purposes of loss functions as described infurther detail below. Any vectors as described herein may be scaled,such that each vector represents each attribute along an equivalentscale of values. Each vector may be “normalized,” or divided by a“length” attribute, such as a length attribute 1 as derived using aPythagorean norm:

$\text{1=}\sqrt{\left( {\text{Σ}\text{\_}\left( {\text{i=}0} \right)\text{\textasciicircum n} \vdots {〚\text{a\_i}〛}\text{\textasciicircum2}} \right)},$

where ai is attribute number i of the vector. Scaling and/ornormalization may function to make vector comparison independent ofabsolute quantities of attributes, while preserving any dependency onsimilarity of attributes; this may, for instance, be advantageous wherecases represented in training data are represented by differentquantities of samples, which may result in proportionally equivalentvectors with divergent values.

Still referring to FIG. 1 , In some embodiments, classifying receivedinformation relating to an element of posting data to user identifier124 of the plurality of user identifiers 124 may also includecalculating a relevance score, as a function of the plurality of useraction data 128, a machine learning algorithm, and classifying thereceived information as a function of the relevance score. As used inthis disclosure, a “relevance score” is a numerical value aggregate of aplurality of sub-scores, as described below, as it relates to a useridentifier 124 overall relevance to posting data 136 for purposes ofrevealing relevant user identifiers 124 to a posting generator. As afunction of the data bins outputted by the classification algorithm, therelevance score may be a numerical value representing the level ofcompatibility or accuracy in linguistic matching to posting data 136. Insome embodiments, calculating the relevance score may include using amachine learning process to calculate and combine a plurality ofsub-scores of an element of action and user role 132 of user identifiers124 based on relevance to the posting data. In some embodiments, themachine learning process may include a data bin from the classifieralgorithm as a machine learning algorithm input and the sub-score as analgorithm output. For example, and element of action data may be theinternship experience of a web developer, the algorithm may then comparethe element of action data 128 to the previously matched element ofposting data 136 to generate sub-scores at least based on acompatibility criterion. In some embodiments, sub-scores may be basedon, for example, textual completeness of action data 128, adequacy ineducation and experience of the user, magnitude of linguistic matching,salary requirements, geographical location, potential start dates,transportation/commute reliability, housing/relocation requirements andthe like. For example, sub scores generated for a web developer mayreflect that even though their action data matches the posting data,their relevance to the posting generator is low based on the webdeveloper’s salary and relocation requirements that do not align withthe posting generator. In some embodiments, calculating and combiningthe plurality of sub-scores may include a fuzzy inference system asdisclosed further below.

Still referring to FIG. 1 , Processor 104 and/or computing device 112 isconfigured to reveal user identifier 124 to a posting generator. In someembodiments, revealing user identifier 124 to the posting generatorincludes decrypting user identifier 124 stored on immutable sequentiallisting 120 and transmitting decrypted user identifier 124 to theposting generator. Transmission may occur via electronic notification toposting generator device 144. As used in this disclosure, a “postinggenerator device” is a device that is used for audio, video, or textcommunication or any other type of computer or computer-like instrument.In some embodiments, computing device 112 may only reveal the mostrelevant user identifiers 148. For example, relevant user identifiers148 may be sorted from most relevant to a job posting to least relevantto a job posting, using any machine learning algorithm as disclosed tocalculate ranking. In this case, the computing device may only reveal afixed number of relevant user identifiers 148. As a non-limitingexample, the computing device may only reveal the first 5 useridentifiers 148. In other embodiments, relevant user identifiers 148 maybe revealed based on their associated relevance score. For example,computing device 112 may only reveal user identifiers 124 with anassociated relevance score that is over a relevance score threshold.

Referring now to FIG. 2 , an exemplary embodiment of an immutablesequential listing is illustrated. An immutable sequential listing maybe, include and/or implement an immutable ledger, where data entriesthat have been posted to the immutable sequential listing cannot bealtered. Data elements are listing in immutable sequential listing; dataelements may include any form of data, including textual data, imagedata, encrypted data, cryptographically hashed data, and the like. Dataelements may include, without limitation, one or more at least adigitally signed assertions. In one embodiment, a digitally signedassertion 204 is a collection of textual data signed using a secureproof as described in further detail below; secure proof may include,without limitation, a digital signature as described above. Collectionof textual data may contain any textual data, including withoutlimitation American Standard Code for Information Interchange (ASCII),Unicode, or similar computer-encoded textual data, any alphanumericdata, punctuation, diacritical mark, or any character or other markingused in any writing system to convey information, in any form, includingany plaintext or cyphertext data; in an embodiment, collection oftextual data may be encrypted, or may be a hash of other data, such as aroot or node of a Merkle tree or hash tree, or a hash of any otherinformation desired to be recorded in some fashion using a digitallysigned assertion 204. In an embodiment, collection of textual datastates that the owner of a certain transferable item represented in adigitally signed assertion 204 register is transferring that item to theowner of an address. A digitally signed assertion 204 may be signed by adigital signature created using the private key associated with theowner’s public key, as described above.

Still referring to FIG. 2 , a digitally signed assertion 204 maydescribe a transfer of virtual currency, such as crypto-currency asdescribed below. The virtual currency may be a digital currency. Item ofvalue may be a transfer of trust, for instance represented by astatement vouching for the identity or trustworthiness of the firstentity. Item of value may be an interest in a fungible negotiablefinancial instrument representing ownership in a public or privatecorporation, a creditor relationship with a governmental body or acorporation, rights to ownership represented by an option, derivativefinancial instrument, commodity, debt-backed security such as a bond ordebenture or other security as described in further detail below. Aresource may be a physical machine e.g., a ride share vehicle or anyother asset. A digitally signed assertion 204 may describe the transferof a physical good; for instance, a digitally signed assertion 204 maydescribe the sale of a product. In some embodiments, a transfernominally of one item may be used to represent a transfer of anotheritem; for instance, a transfer of virtual currency may be interpreted asrepresenting a transfer of an access right; conversely, where the itemnominally transferred is something other than virtual currency, thetransfer itself may still be treated as a transfer of virtual currency,having value that depends on many potential factors including the valueof the item nominally transferred and the monetary value attendant tohaving the output of the transfer moved into a particular user’scontrol. The item of value may be associated with a digitally signedassertion 204 by means of an exterior protocol, such as the COLOREDCOINS created according to protocols developed by The Colored CoinsFoundation, the MASTERCOIN protocol developed by the MastercoinFoundation, or the ETHEREUM platform offered by the Stiftung EthereumFoundation of Baar, Switzerland, the Thunder protocol developed byThunder Consensus, or any other protocol.

Still referring to FIG. 2 , in one embodiment, an address is a textualdatum identifying the recipient of virtual currency or another item ofvalue in a digitally signed assertion 204. In some embodiments, addressis linked to a public key, the corresponding private key of which isowned by the recipient of a digitally signed assertion 204. Forinstance, address may be the public key. Address may be arepresentation, such as a hash, of the public key. Address may be linkedto the public key in memory of a computing device, for instance via a“wallet shortener” protocol. Where address is linked to a public key, atransferee in a digitally signed assertion 204 may record a subsequent adigitally signed assertion 204 transferring some or all of the valuetransferred in the first a digitally signed assertion 204 to a newaddress in the same manner. A digitally signed assertion 204 may containtextual information that is not a transfer of some item of value inaddition to, or as an alternative to, such a transfer. For instance, asdescribed in further detail below, a digitally signed assertion 204 mayindicate a confidence level associated with a distributed storage nodeas described in further detail below.

In an embodiment, and still referring to FIG. 2 immutable sequentiallisting records a series of at least a posted content in a way thatpreserves the order in which the at least a posted content took place.Temporally sequential listing may be accessible at any of varioussecurity settings; for instance, and without limitation, temporallysequential listing may be readable and modifiable publicly, may bepublicly readable but writable only by entities and/or devices havingaccess privileges established by password protection, confidence level,or any device authentication procedure or facilities described herein,or may be readable and/or writable only by entities and/or deviceshaving such access privileges. Access privileges may exist in more thanone level, including, without limitation, a first access level orcommunity of permitted entities and/or devices having ability to read,and a second access level or community of permitted entities and/ordevices having ability to write; first and second community may beoverlapping or non-overlapping. In an embodiment, posted content and/orimmutable sequential listing may be stored as one or more zero knowledgesets (ZKS), Private Information Retrieval (PIR) structure, or any otherstructure that allows checking of membership in a set by querying withspecific properties. Such database may incorporate protective measuresto ensure that malicious actors may not query the database repeatedly inan effort to narrow the members of a set to reveal uniquely identifyinginformation of a given posted content.

Still referring to FIG. 2 , immutable sequential listing may preservethe order in which the at least a posted content took place by listingthem in chronological order; alternatively or additionally, immutablesequential listing may organize digitally signed assertions 204 intosub-listings 208 such as “blocks” in a blockchain, which may bethemselves collected in a temporally sequential order; digitally signedassertions 204 within a sub-listing 208 may or may not be temporallysequential. The ledger may preserve the order in which at least a postedcontent took place by listing them in sub-listings 208 and placing thesub-listings 208 in chronological order. The immutable sequentiallisting may be a distributed, consensus-based ledger, such as thoseoperated according to the protocols promulgated by Ripple Labs, Inc., ofSan Francisco, Calif., or the Stellar Development Foundation, of SanFrancisco, Calif, or of Thunder Consensus. In some embodiments, theledger is a secured ledger; in one embodiment, a secured ledger is aledger having safeguards against alteration by unauthorized parties. Theledger may be maintained by a proprietor, such as a system administratoron a server, that controls access to the ledger; for instance, the useraccount controls may allow contributors to the ledger to add at least aposted content to the ledger, but may not allow any users to alter atleast a posted content that have been added to the ledger. In someembodiments, ledger is cryptographically secured; in one embodiment, aledger is cryptographically secured where each link in the chaincontains encrypted or hashed information that makes it practicallyinfeasible to alter the ledger without betraying that alteration hastaken place, for instance by requiring that an administrator or otherparty sign new additions to the chain with a digital signature.Immutable sequential listing may be incorporated in, stored in, orincorporate, any suitable data structure, including without limitationany database, datastore, file structure, distributed hash table,directed acyclic graph or the like. In some embodiments, the timestampof an entry is cryptographically secured and validated via trusted time,either directly on the chain or indirectly by utilizing a separatechain. In one embodiment the validity of timestamp is provided using atime stamping authority as described in the RFC 3161 standard fortrusted timestamps, or in the ANSI ASC x9.95 standard. In anotherembodiment, the trusted time ordering is provided by a group of entitiescollectively acting as the time stamping authority with a requirementthat a threshold number of the group of authorities sign the timestamp.

In some embodiments, and with continued reference to FIG. 2 , immutablesequential listing, once formed, may be inalterable by any party, nomatter what access rights that party possesses. For instance, immutablesequential listing may include a hash chain, in which data is addedduring a successive hashing process to ensure non-repudiation. Immutablesequential listing may include a block chain. In one embodiment, a blockchain is immutable sequential listing that records one or more new atleast a posted content in a data item known as a sub-listing 208 or“block.” An example of a block chain is the BITCOIN block chain used torecord BITCOIN transactions and values. Sub-listings 208 may be createdin a way that places the sub-listings 208 in chronological order andlink each sub-listing 208 to a previous sub-listing 208 in thechronological order so that any computing device may traverse thesub-listings 208 in reverse chronological order to verify any at least aposted content listed in the block chain. Each new sub-listing 208 maybe required to contain a cryptographic hash describing the previoussub-listing 208. In some embodiments, the block chain contains a singlefirst sub-listing 208 sometimes known as a “genesis block.”

Still referring to FIG. 2 , the creation of a new sub-listing 208 may becomputationally expensive; for instance, the creation of a newsub-listing 208 may be designed by a “proof of work” protocol acceptedby all participants in forming the immutable sequential listing to takea powerful set of computing devices a certain period of time to produce.Where one sub-listing 208 takes less time for a given set of computingdevices to produce the sub-listing 208 protocol may adjust the algorithmto produce the next sub-listing 208 so that it will require more steps;where one sub-listing 208 takes more time for a given set of computingdevices to produce the sub-listing 208 protocol may adjust the algorithmto produce the next sub-listing 208 so that it will require fewer steps.As an example, protocol may require a new sub-listing 208 to contain acryptographic hash describing its contents; the cryptographic hash maybe required to satisfy a mathematical condition, achieved by having thesub-listing 208 contain a number, called a nonce, whose value isdetermined after the fact by the discovery of the hash that satisfiesthe mathematical condition. Continuing the example, the protocol may beable to adjust the mathematical condition so that the discovery of thehash describing a sub-listing 208 and satisfying the mathematicalcondition requires more or less steps, depending on the outcome of theprevious hashing attempt. Mathematical condition, as an example, mightbe that the hash contains a certain number of leading zeros and ahashing algorithm that requires more steps to find a hash containing agreater number of leading zeros, and fewer steps to find a hashcontaining a lesser number of leading zeros. In some embodiments,production of a new sub-listing 208 according to the protocol is knownas “mining.” The creation of a new sub-listing 208 may be designed by a“proof of stake” protocol as will be apparent to those skilled in theart upon reviewing the entirety of this disclosure.

Continuing to refer to FIG. 2 , in some embodiments, protocol alsocreates an incentive to mine new sub-listings 208. The incentive may befinancial; for instance, successfully mining a new sub-listing 208 mayresult in the person or entity that mines the sub-listing 208 receivinga predetermined amount of currency. The currency may be fiat currency.Currency may be cryptocurrency as defined below. In other embodiments,incentive may be redeemed for particular products or services; theincentive may be a gift certificate with a particular business, forinstance. In some embodiments, incentive is sufficiently attractive tocause participants to compete for the incentive by trying to race eachother to the creation of sub-listings 208. Each sub-listing 208 createdin immutable sequential listing may contain a record or at least aposted content describing one or more addresses that receive anincentive, such as virtual currency, as the result of successfullymining the sub-listing 208.

With continued reference to FIG. 2 , where two entities simultaneouslycreate new sub-listings 208, immutable sequential listing may develop afork; protocol may determine which of the two alternate branches in thefork is the valid new portion of the immutable sequential listing byevaluating, after a certain amount of time has passed, which branch islonger. “Length” may be measured according to the number of sub-listings208 in the branch. Length may be measured according to the totalcomputational cost of producing the branch. Protocol may treat only atleast a posted content contained the valid branch as valid at least aposted content. When a branch is found invalid according to thisprotocol, at least a posted content registered in that branch may berecreated in a new sub-listing 208 in the valid branch; the protocol mayreject “double spending” at least a posted content that transfer thesame virtual currency that another at least a posted content in thevalid branch has already transferred. As a result, in some embodimentsthe creation of fraudulent at least a posted content requires thecreation of a longer immutable sequential listing branch by the entityattempting the fraudulent at least a posted content than the branchbeing produced by the rest of the participants; as long as the entitycreating the fraudulent at least a posted content is likely the only onewith the incentive to create the branch containing the fraudulent atleast a posted content, the computational cost of the creation of thatbranch may be practically infeasible, guaranteeing the validity of allat least a posted content in the immutable sequential listing.

Still referring to FIG. 2 , additional data linked to at least a postedcontent may be incorporated in sub-listings 208 in the immutablesequential listing; for instance, data may be incorporated in one ormore fields recognized by block chain protocols that permit a person orcomputer forming a at least a posted content to insert additional datain the immutable sequential listing. In some embodiments, additionaldata is incorporated in an unspendable at least a posted content field.For instance, the data may be incorporated in an OP_RETURN within theBITCOIN block chain. In other embodiments, additional data isincorporated in one signature of a multi-signature at least a postedcontent. In an embodiment, a multi-signature at least a posted contentis at least a posted content to two or more addresses. In someembodiments, the two or more addresses are hashed together to form asingle address, which is signed in the digital signature of the at leasta posted content. In other embodiments, the two or more addresses areconcatenated. In some embodiments, two or more addresses may be combinedby a more complicated process, such as the creation of a Merkle tree orthe like. In some embodiments, one or more addresses incorporated in themulti-signature at least a posted content are typical crypto-currencyaddresses, such as addresses linked to public keys as described above,while one or more additional addresses in the multi-signature at least aposted content contain additional data related to the at least a postedcontent; for instance, the additional data may indicate the purpose ofthe at least a posted content, aside from an exchange of virtualcurrency, such as the item for which the virtual currency was exchanged.In some embodiments, additional information may include networkstatistics for a given node of network, such as a distributed storagenode, e.g. the latencies to nearest neighbors in a network graph, theidentities or identifying information of neighboring nodes in thenetwork graph, the trust level and/or mechanisms of trust (e.g.certificates of physical encryption keys, certificates of softwareencryption keys, (in non-limiting example certificates of softwareencryption may indicate the firmware version, manufacturer, hardwareversion and the like), certificates from a trusted third party,certificates from a decentralized anonymous authentication procedure,and other information quantifying the trusted status of the distributedstorage node) of neighboring nodes in the network graph, IP addresses,GPS coordinates, and other information informing location of the nodeand/or neighboring nodes, geographically and/or within the networkgraph. In some embodiments, additional information may include historyand/or statistics of neighboring nodes with which the node hasinteracted. In some embodiments, this additional information may beencoded directly, via a hash, hash tree or other encoding.

With continued reference to FIG. 2 , in some embodiments, virtualcurrency is traded as a crypto-currency. In one embodiment, acrypto-currency is a digital, currency such as Bitcoins, Peercoins,Namecoins, and Litecoins. Crypto-currency may be a clone of anothercrypto-currency. The crypto-currency may be an “alt-coin.”Crypto-currency may be decentralized, with no particular entitycontrolling it; the integrity of the crypto-currency may be maintainedby adherence by its participants to established protocols for exchangeand for production of new currency, which may be enforced by softwareimplementing the crypto-currency. Crypto-currency may be centralized,with its protocols enforced or hosted by a particular entity. Forinstance, crypto-currency may be maintained in a centralized ledger, asin the case of the XRP currency of Ripple Labs, Inc., of San Francisco,Calif. In lieu of a centrally controlling authority, such as a nationalbank, to manage currency values, the number of units of a particularcrypto-currency may be limited; the rate at which units ofcrypto-currency enter the market may be managed by a mutuallyagreed-upon process, such as creating new units of currency whenmathematical puzzles are solved, the degree of difficulty of the puzzlesbeing adjustable to control the rate at which new units enter themarket. Mathematical puzzles may be the same as the algorithms used tomake productions of sub-listings 208 in a block chain computationallychallenging; the incentive for producing sub-listings 208 may includethe grant of new crypto-currency to the miners. Quantities ofcrypto-currency may be exchanged using at least a posted content asdescribed above.

Referring now to FIG. 3 , an exemplary embodiment of a machine-learningmodule 300 that may perform one or more machine-learning processes asdescribed in this disclosure is illustrated. Machine-learning module mayperform determinations, classification, and/or analysis steps, methods,processes, or the like as described in this disclosure using machinelearning processes. A “machine learning process,” as used in thisdisclosure, is a process that automatedly uses training data 304 togenerate an algorithm that will be performed by a computingdevice/module to produce outputs 308 given data provided as inputs 312;this is in contrast to a non-machine learning software program where thecommands to be executed are determined in advance by a user and writtenin a programming language.

Still referring to FIG. 3 , “training data,” as used herein, is datacontaining correlations that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data 304 may include aplurality of data entries, each entry representing a set of dataelements that were recorded, received, and/or generated together; dataelements may be correlated by shared existence in a given data entry, byproximity in a given data entry, or the like. Multiple data entries intraining data 304 may evince one or more trends in correlations betweencategories of data elements; for instance, and without limitation, ahigher value of a first data element belonging to a first category ofdata element may tend to correlate to a higher value of a second dataelement belonging to a second category of data element, indicating apossible proportional or other mathematical relationship linking valuesbelonging to the two categories. Multiple categories of data elementsmay be related in training data 304 according to various correlations;correlations may indicate causative and/or predictive links betweencategories of data elements, which may be modeled as relationships suchas mathematical relationships by machine-learning processes as describedin further detail below. Training data 304 may be formatted and/ororganized by categories of data elements, for instance by associatingdata elements with one or more descriptors corresponding to categoriesof data elements. As a non-limiting example, training data 304 mayinclude data entered in standardized forms by persons or processes, suchthat entry of a given data element in a given field in a form may bemapped to one or more descriptors of categories. Elements in trainingdata 304 may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training data304 may be provided in fixed-length formats, formats linking positionsof data to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),JavaScript Object Notation (JSON), or the like, enabling processes ordevices to detect categories of data.

Alternatively or additionally, and continuing to refer to FIG. 3 ,training data 304 may include one or more elements that are notcategorized; that is, training data 304 may not be formatted or containdescriptors for some elements of data. Machine-learning algorithmsand/or other processes may sort training data 304 according to one ormore categorizations using, for instance, natural language processingalgorithms, tokenization, detection of correlated values in raw data andthe like; categories may be generated using correlation and/or otherprocessing algorithms. As a non-limiting example, in a corpus of text,phrases making up a number “n” of compound words, such as nouns modifiedby other nouns, may be identified according to a statisticallysignificant prevalence of n-grams containing such words in a particularorder; such an n-gram may be categorized as an element of language suchas a “word” to be tracked similarly to single words, generating a newcategory as a result of statistical analysis. Similarly, in a data entryincluding some textual data, a person’s name may be identified byreference to a list, dictionary, or other compendium of terms,permitting ad-hoc categorization by machine-learning algorithms, and/orautomated association of data in the data entry with descriptors or intoa given format. The ability to categorize data entries automatedly mayenable the same training data 304 to be made applicable for two or moredistinct machine-learning algorithms as described in further detailbelow. Training data 304 used by machine-learning module 300 maycorrelate any input data as described in this disclosure to any outputdata as described in this disclosure.

Further referring to FIG. 3 , training data may be filtered, sorted,and/or selected using one or more supervised and/or unsupervisedmachine-learning processes and/or models as described in further detailbelow; such models may include without limitation a training dataclassifier 316. Training data classifier 316 may include a “classifier,”which as used in this disclosure is a machine-learning model as definedbelow, such as a mathematical model, neural net, or program generated bya machine learning algorithm known as a “classification algorithm,” asdescribed in further detail below, that sorts inputs into categories orbins of data, outputting the categories or bins of data and/or labelsassociated therewith. A classifier may be configured to output at leasta datum that labels or otherwise identifies a set of data that areclustered together, found to be close under a distance metric asdescribed below, or the like. Machine-learning module 300 may generate aclassifier using a classification algorithm, defined as a processeswhereby a computing device and/or any module and/or component operatingthereon derives a classifier from training data 304. Classification maybe performed using, without limitation, linear classifiers such aswithout limitation logistic regression and/or naive Bayes classifiers,nearest neighbor classifiers such as k-nearest neighbors classifiers,support vector machines, least squares support vector machines, fisher’slinear discriminant, quadratic classifiers, decision trees, boostedtrees, random forest classifiers, learning vector quantization, and/orneural network-based classifiers.

Still referring to FIG. 3 , machine-learning module 300 may beconfigured to perform a lazy-learning process 320 and/or protocol, whichmay alternatively be referred to as a “lazy loading” or“call-when-needed” process and/or protocol, may be a process wherebymachine learning is conducted upon receipt of an input to be convertedto an output, by combining the input and training set to derive thealgorithm to be used to produce the output on demand. For instance, aninitial set of simulations may be performed to cover an initialheuristic and/or “first guess” at an output and/or relationship. As anon-limiting example, an initial heuristic may include a ranking ofassociations between inputs and elements of training data 304. Heuristicmay include selecting some number of highest-ranking associations and/ortraining data 304 elements. Lazy learning may implement any suitablelazy learning algorithm, including without limitation a K-nearestneighbors algorithm, a lazy naïve Bayes algorithm, or the like; personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various lazy-learning algorithms that may be applied togenerate outputs as described in this disclosure, including withoutlimitation lazy learning applications of machine-learning algorithms asdescribed in further detail below.

Alternatively or additionally, and with continued reference to FIG. 3 ,machine-learning processes as described in this disclosure may be usedto generate machine-learning models 324. A “machine-learning model,” asused in this disclosure, is a mathematical and/or algorithmicrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process including withoutlimitation any process as described above, and stored in memory; aninput is submitted to a machine-learning model 324 once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processesto calculate an output datum. As a further non-limiting example, amachine-learning model 324 may be generated by creating an artificialneural network, such as a convolutional neural network including aninput layer of nodes, one or more intermediate layers, and an outputlayer of nodes. Connections between nodes may be created via the processof “training” the network, in which elements from a training data 304set are applied to the input nodes, a suitable training algorithm (suchas Levenberg-Marquardt, conjugate gradient, simulated annealing, orother algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning.

Still referring to FIG. 3 , machine-learning algorithms may include atleast a supervised machine-learning process 328. At least a supervisedmachine-learning process 328, as defined herein, include algorithms thatreceive a training set relating a number of inputs to a number ofoutputs, and seek to find one or more mathematical relations relatinginputs to outputs, where each of the one or more mathematical relationsis optimal according to some criterion specified to the algorithm usingsome scoring function. For instance, a supervised learning algorithm mayinclude inputs and outputs described through this disclosure, and ascoring function representing a desired form of relationship to bedetected between inputs and outputs; scoring function may, for instance,seek to maximize the probability that a given input and/or combinationof elements inputs is associated with a given output to minimize theprobability that a given input is not associated with a given output.Scoring function may be expressed as a risk function representing an“expected loss” of an algorithm relating inputs to outputs, where lossis computed as an error function representing a degree to which aprediction generated by the relation is incorrect when compared to agiven input-output pair provided in training data 304. Persons skilledin the art, upon reviewing the entirety of this disclosure, will beaware of various possible variations of at least a supervisedmachine-learning process 328 that may be used to determine relationbetween inputs and outputs. Supervised machine-learning processes mayinclude classification algorithms as defined above.

Further referring to FIG. 3 , machine learning processes may include atleast an unsupervised machine-learning processes 332. An unsupervisedmachine-learning process, as used herein, is a process that derivesinferences in datasets without regard to labels; as a result, anunsupervised machine-learning process may be free to discover anystructure, relationship, and/or correlation provided in the data.Unsupervised processes may not require a response variable; unsupervisedprocesses may be used to find interesting patterns and/or inferencesbetween variables, to determine a degree of correlation between two ormore variables, or the like.

Still referring to FIG. 3 , machine-learning module 300 may be designedand configured to create a machine-learning model 324 using techniquesfor development of linear regression models. Linear regression modelsmay include ordinary least squares regression, which aims to minimizethe square of the difference between predicted outcomes and actualoutcomes according to an appropriate norm for measuring such adifference (e.g., a vector-space distance norm); coefficients of theresulting linear equation may be modified to improve minimization.Linear regression models may include ridge regression methods, where thefunction to be minimized includes the least-squares function plus termmultiplying the square of each coefficient by a scalar amount topenalize large coefficients. Linear regression models may include leastabsolute shrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g., a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 3 , machine-learning algorithms may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminate analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors algorithms. Machine-learning algorithms may includevarious forms of latent space regularization such as variationalregularization. Machine-learning algorithms may include Gaussianprocesses such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naïve Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized tress, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

Referring now to FIG. 4 , an exemplary embodiment of neural network 400is illustrated. A neural network 400 also known as an artificial neuralnetwork, is a network of “nodes,” or data structures having one or moreinputs, one or more outputs, and a function determining outputs based oninputs. Such nodes may be organized in a network, such as withoutlimitation a convolutional neural network, including an input layer ofnodes 404, one or more intermediate layers 408, and an output layer ofnodes 412. Connections between nodes may be created via the process of“training” the network, in which elements from a training dataset areapplied to the input nodes, a suitable training algorithm (such asLevenberg-Marquardt, conjugate gradient, simulated annealing, or otheralgorithms) is then used to adjust the connections and weights betweennodes in adjacent layers of the neural network to produce the desiredvalues at the output nodes. This process is sometimes referred to asdeep learning. Connections may run solely from input nodes toward outputnodes in a “feed-forward” network, or may feed outputs of one layer backto inputs of the same or a different layer in a “recurrent network.”

Referring now to FIG. 5 , an exemplary embodiment of a node of a neuralnetwork is illustrated. A node may include, without limitation aplurality of inputs x_(i) that may receive numerical values from inputsto a neural network containing the node and/or from other nodes. Nodemay perform a weighted sum of inputs using weights w_(i) that aremultiplied by respective inputs x_(i). Additionally or alternatively, abias b may be added to the weighted sum of the inputs such that anoffset is added to each unit in the neural network layer that isindependent of the input to the layer. The weighted sum may then beinput into a function φ, which may generate one or more outputs y.Weight w_(i) applied to an input x_(i) may indicate whether the input is“excitatory,” indicating that it has strong influence on the one or moreoutputs y, for instance by the corresponding weight having a largenumerical value, and/or a “inhibitory,” indicating it has a weak effectinfluence on the one more inputs y, for instance by the correspondingweight having a small numerical value. The values of weights w_(i) maybe determined by training a neural network using training data, whichmay be performed using any suitable process as described above.

Referring to FIG. 6 , an exemplary embodiment of fuzzy set comparisonfor calculating and combining sub-scores 600 is illustrated. A firstfuzzy set 604 may be represented, without limitation, according to afirst membership function 608 representing a probability that an inputfalling on a first range of values 612 is a member of the first fuzzyset 604, where the first membership function 608 has values on a rangeof probabilities such as without limitation the interval [0,1], and anarea beneath the first membership function 608 may represent a set ofvalues within first fuzzy set 604. Although first range of values 612 isillustrated for clarity in this exemplary depiction as a range on asingle number line or axis, first range of values 612 may be defined ontwo or more dimensions, representing, for instance, a Cartesian productbetween a plurality of ranges, curves, axes, spaces, dimensions, or thelike. First membership function 608 may include any suitable functionmapping first range 612 to a probability interval, including withoutlimitation a triangular function defined by two linear elements such asline segments or planes that intersect at or below the top of theprobability interval. As a non-limiting example, triangular membershipfunction may be defined as:

$y\left( {x,a,b,c} \right) = \left\{ \begin{matrix}{0,\mspace{6mu} for\mspace{6mu} x > c\mspace{6mu} and\mspace{6mu} x < a} \\{\frac{x - a}{b - a},for\mspace{6mu} a \leq x < b} \\{\frac{c - x}{c - b},if\mspace{6mu} b < x \leq c}\end{matrix} \right)$

a trapezoidal membership function may be defined as:

$y\left( {x,a,b,c,d} \right) = max\left( {min\left( {\frac{x - a}{b - a},1,\frac{d - x}{d - c}} \right),0} \right)$

a sigmoidal function may be defined as:

$y\left( {x,a,c} \right) = \frac{1}{1 - e^{- a{({x - c})}}}$

a Gaussian membership function may be defined as:

$y\left( {x,c,\sigma} \right) = e^{- \frac{1}{2}{(\frac{x - c}{\sigma})}^{2}}$

and a bell membership function may be defined as:

$y\left( {x,a,b,c} \right) = \left\lbrack {1 + \left| \frac{x - c}{a} \right|^{2b}} \right\rbrack^{- 1}$

Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various alternative or additionalmembership functions that may be used consistently with this disclosure.

Still referring to FIG. 6 , a first fuzzy set may represent any value orcombination of values as described above, including sub-scores based ona compatibility criterion, probabilistic outcome, any resource datum,any niche datum, and/or any combination of the above. A second fuzzyset, which may represent any value which may be represented by firstfuzzy set, may be defined by a second membership function on a secondrange; a second range may be identical and/or overlap with a first rangeand/or may be combined with first range via Cartesian product or thelike to generate a mapping permitting evaluation overlap of a firstfuzzy set and a second fuzzy set. Where first fuzzy set and second fuzzyset have a region that overlaps, a first membership function and asecond membership function may intersect at a point representing aprobability, as defined on probability interval, of a match between afirst fuzzy set and a second fuzzy set. Alternatively or additionally, asingle value of a first and/or a second fuzzy set may be located at alocus on a first range and/or a second range, where a probability ofmembership may be taken by evaluation of a first membership functionand/or a second membership function at that range point. A probabilitymay be compared to a threshold to determine whether a positive match isindicated. A threshold may, in a non-limiting example, represent adegree of match between a first fuzzy set and a second fuzzy set, and/orsingle values therein with each other or with either set, which issufficient for purposes of the matching process; for instance, thresholdmay indicate a sufficient degree of overlap between probabilisticoutcomes and/or predictive prevalence values for combination to occur asdescribed above. There may be multiple thresholds; for instance, asecond threshold may indicate a sufficient match for purposes of apooling threshold as described in this disclosure. Each threshold may beestablished by one or more user inputs. Alternatively or additionally,each threshold may be tuned by a machine-learning and/or statisticalprocess, for instance and without limitation as described in furtherdetail below.

Still referring to FIG. 6 , in an embodiment, a degree of match betweenfuzzy sets may be used to rank one user identifier 124 datum againstanother. For instance, if two user identifiers 124 datums have fuzzysets matching a probabilistic outcome fuzzy set by having a degree ofoverlap exceeding a threshold, a computing device may further rank thetwo user identifiers 124 datums by ranking an entity expertise datumhaving a higher degree of match more highly than an entity expertisedatum having a lower degree of match. Where multiple fuzzy matches areperformed, degrees of match for each respective fuzzy set may becomputed and aggregated through, for instance, addition, averaging, orthe like, to determine an overall degree of match, which may be used torank entity expertise data; selection between two or more matchingentity expertise datums may be performed by selection of ahighest-ranking entity expertise datum, and/or multiple predictiveprevalence values may be presented to a user in order of ranking. Acomputing device may use fuzzy logic to classify and/or group two ormore data entries of the classification algorithm described above.

Referring now to FIG. 7 , illustrated is a flow diagram of an exemplarymethod for revealing user identifiers on an immutable sequential listingto a posting generator. At step 705, method includes using a computingdevice to store on an immutable sequential listing, a plurality of useridentifiers, wherein each user identifier of the plurality of useridentifiers is associated with the same user, each user identifier ofthe plurality of user identifiers is associated with a plurality ofaction data, and each of the plurality of user identifiers is associatedwith a user role of the user, for example and with reference to FIG. 1 .The computing device may be any computing device described throughoutthis disclosure, for example and with reference to FIG. 1 . Thecomputing device may perform determinations, classification, and/oranalysis steps, methods, processes, or the like as described in thisdisclosure using machine learning processes, for example and withreference to FIGS. 1- 6 . Each user identifier may be associated with adifferent user role which the user may perform. For example, user mayhave two or more professional affiliations such as in STEM and patentlaw, two or more educational affiliations such as teaching and studying,two or more community affiliations such as in healthcare and coachingsports, and the like, among others. User identifiers may be based oneach of such user roles. User identifiers may also be based on differentsectors of a user’s life, for example, an identifier for education, anidentifier for personal life, an identifier for work history, and thelike, among others.

Still referring to FIG. 7 , at step 710, method includes using acomputing device to receive information relating to an element ofposting data associated with a posting generator. In some embodimentsposting data, as defined in FIG. 1 , posting data may be stored in adatabase connected to the computing device using any network interfacedescribed throughout this disclosure. For example a posting generatormay upload documents containing the user requirements for a job to aposting database. The computing device may then access those documentsin the database through a network to download a document and parseelements of posting data using a language processor module, for exampleand with reference to FIG. 1

Still referring to FIG. 7 , at step 715, method includes using acomputing device to classify, as a function of the received information,the information to a user identifier of the plurality of useridentifiers as a function of the plurality of action data associatedwith the user identifier. In some embodiments classifying the receivedinformation may include a language processor module to match each termof the posting data to a plurality of synonyms of terms contained in theaction data and user role associated with a user identifier of theplurality of user identifiers, for example and with refence to FIG. 1 .The language processor module may be any module described throughoutthis disclosure. This process may be repeated for the total plurality ofuser identifiers stored on the immutable sequential listing. In someembodiments, classifying the received information may also include amachine learning classifier to match posting data to a particular useridentifier of the plurality of user identifier as function of theplurality of synonyms, for example and with reference to FIG. 1 . Methodmay include a classification algorithm as disclosed in FIGS. 1 and 2 .In some embodiments, the classifier training data may only contain datafrom user identifiers that presented synonym matches to the to thereceived information in the posting data, instead of the total pluralityof user identifiers stored on the immutable sequential listing. In someembodiments, classifying the received information relating to an elementof posting data to a user identifier of the plurality of useridentifiers may also include calculating a relevance score as a functionof the plurality of user action data and a machine learning algorithm,and classifying the received information as a function of the relevancescore, as disclosed in FIG. 1 . As a function on the data bins outputtedby the classification algorithm, the relevance score may be a numericalvalue representing the level of compatibility or accuracy in linguisticmatching to the posting data.

Still referring to FIG. 7 , in some embodiments, calculating a relevancescore includes using a machine learning process to calculate and combinea plurality of sub-scores of an element of action and user role of auser identifiers based on relevance to the posting data. In someembodiments, the machine learning process may include a data bin fromthe classifier algorithm as a machine learning algorithm input and thesub-score as an algorithm output, for example and with reference toFIGS. 1 and 2 . In some embodiments, sub-scores may be based on, forexample, textual completeness of action data, adequacy in education andexperience of the user, and the like. In some embodiments, calculatingand combining the plurality of sub-scores may include a fuzzy inference,for example and with reference to FIG. 6 .

Still referring to FIG. 7 , at step 720, method includes using acomputing device to reveal a user identifier to the posting generator.In some embodiments, revealing the user identifier to the postinggenerator includes decrypting the user identifier stored on theimmutable sequential listing and transmitting the decrypted useridentifier to the posting generator. Transmission may occur viaelectronic notification to a post generator device, as defined in FIG. 1. In some embodiments, the computing device may only reveal the mostrelevant user identifiers. For example, the relevant user identifiersmay be sorted from most relevant to a job posting to least relevant to ajob posting, using any machine learning algorithm as disclosed tocalculate ranking. In this case, the computing device may only reveal afixed number of identities. As a non-limiting example, the computingdevice may only reveal the first 5 identities. In other embodiments, therelevant identities may be revealed based on their associated relevancescore. For example, the computing device may only reveal the useridentities with an associated relevance score that is over a relevancescore threshold.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 8 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 800 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 800 includes a processor 804 and a memory808 that communicate with each other, and with other components, via abus 812. Bus 812 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Processor 804 may include any suitable processor, such as withoutlimitation a processor incorporating logical circuitry for performingarithmetic and logical operations, such as an arithmetic and logic unit(ALU), which may be regulated with a state machine and directed byoperational inputs from memory and/or sensors; processor 804 may beorganized according to Von Neumann and/or Harvard architecture as anon-limiting example. Processor 804 may include, incorporate, and/or beincorporated in, without limitation, a microcontroller, microprocessor,digital signal processor (DSP), Field Programmable Gate Array (FPGA),Complex Programmable Logic Device (CPLD), Graphical Processing Unit(GPU), general purpose GPU, Tensor Processing Unit (TPU), analog ormixed signal processor, Trusted Platform Module (TPM), a floating pointunit (FPU), and/or system on a chip (SoC).

Memory 808 may include various components (e.g., machine-readable media)including, but not limited to, a random-access memory component, a readonly component, and any combinations thereof. In one example, a basicinput/output system 816 (BIOS), including basic routines that help totransfer information between elements within computer system 800, suchas during start-up, may be stored in memory 808. Memory 808 may alsoinclude (e.g., stored on one or more machine-readable media)instructions (e.g., software) 820 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 808 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 800 may also include a storage device 824. Examples of astorage device (e.g., storage device 824) include, but are not limitedto, a hard disk drive, a magnetic disk drive, an optical disc drive incombination with an optical medium, a solid-state memory device, and anycombinations thereof. Storage device 824 may be connected to bus 812 byan appropriate interface (not shown). Example interfaces include, butare not limited to, SCSI, advanced technology attachment (ATA), serialATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and anycombinations thereof. In one example, storage device 824 (or one or morecomponents thereof) may be removably interfaced with computer system 800(e.g., via an external port connector (not shown)). Particularly,storage device 824 and an associated machine-readable medium 828 mayprovide nonvolatile and/or volatile storage of machine-readableinstructions, data structures, program modules, and/or other data forcomputer system 800. In one example, software 820 may reside, completelyor partially, within machine-readable medium 828. In another example,software 820 may reside, completely or partially, within processor 804.

Computer system 800 may also include an input device 832. In oneexample, a user of computer system 800 may enter commands and/or otherinformation into computer system 800 via input device 832. Examples ofan input device 832 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 832may be interfaced to bus 812 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 812, and any combinations thereof. Input device 832 mayinclude a touch screen interface that may be a part of or separate fromdisplay 836, discussed further below. Input device 832 may be utilizedas a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 800 via storage device 824 (e.g., a removable disk drive, a flashdrive, etc.) and/or network interface device 840. A network interfacedevice, such as network interface device 840, may be utilized forconnecting computer system 800 to one or more of a variety of networks,such as network 844, and one or more remote devices 848 connectedthereto. Examples of a network interface device include, but are notlimited to, a network interface card (e.g., a mobile network interfacecard, a LAN card), a modem, and any combination thereof. Examples of anetwork include, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network, such as network 844,may employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, software 820,etc.) may be communicated to and/or from computer system 800 via networkinterface device 840.

Computer system 800 may further include a video display adapter 852 forcommunicating a displayable image to a display device, such as displaydevice 836. Examples of a display device include, but are not limitedto, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasmadisplay, a light emitting diode (LED) display, and any combinationsthereof. Display adapter 852 and display device 836 may be utilized incombination with processor 804 to provide graphical representations ofaspects of the present disclosure. In addition to a display device,computer system 800 may include one or more other peripheral outputdevices including, but not limited to, an audio speaker, a printer, andany combinations thereof. Such peripheral output devices may beconnected to bus 812 via a peripheral interface 856. Examples of aperipheral interface include, but are not limited to, a serial port, aUSB connection, a FIREWIRE connection, a parallel connection, and anycombinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods,systems, and software according to the present disclosure. Accordingly,this description is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

What is claimed is:
 1. An apparatus for revealing user identifiers on animmutable sequential listing to a posting generator, the apparatuscomprising: at least a processor; and a memory communicatively connectedto the processor, the memory containing instructions configuring the atleast a processor to: store on an immutable sequential listing, aplurality of user identifiers, wherein: each user identifier of theplurality of user identifiers is associated with the same user; eachuser identifier of the plurality of user identifiers is associated witha plurality of action data; and each user identifier of the plurality ofuser identifiers is associated with a user role of the user; receiveinformation relating to an element of posting data associated with aposting generator; classify the received information to a useridentifier of the plurality of user identifiers as a function of theplurality of action data associated with the user identifier, whereinclassifying the received information to the user identifier furthercomprises: training a machine learning algorithm with training data togenerate a first machine learning model, wherein the training datacorrelates posting information data to user action data; and classifyingthe received information to the user identifier using the first machinelearning model; and reveal the user identifier to the posting generator.2. The apparatus of claim 1, wherein classifying the receivedinformation further comprises: determining, using the first machinelearning model, the classification of the received information to theuser identifier, wherein the received information is provided to thefirst machine learning model as an input to output the user identifier;and calculating, using a second machine learning model, a relevancescore for the user identifier as a function of the plurality of actiondata associated with the user identifier and the posting data.
 3. Theapparatus of claim 1, wherein the processor is further configured toauthenticate the posting generator, wherein authenticating the postinggenerator comprises verifying a digital certificate of the postinggenerator.
 4. The apparatus of claim 1, wherein the processor is furtherconfigured to decrypt the user identifier stored on the immutablesequential listing based on the authentication of the posting generator.5. The apparatus of claim 1, wherein revealing the user identifiercomprising revealing the user identifier to the posting generator if therelevance score for the user identifier exceeds a relevance scorethreshold.
 6. The apparatus of claim 5, wherein revealing the useridentifier comprises revealing a fixed number of user identifiers withrelevance scores exceeding the relevance score threshold.
 7. Theapparatus of claim 5, wherein calculating the relevance score comprisescombining a plurality of sub-scores of an element of the action data andthe user role of the user identifiers as a function of relevance of theposting data.
 8. The apparatus of claim 7, wherein calculating therelevance score further comprises: using a machine learning process tocalculate the plurality of sub-scores of the element of action; andcombining the plurality of sub-scores into the relevance score.
 9. Theapparatus of claim 8, wherein an input of the machine-learning processcomprises the plurality of action data and user roles, and an output ofthe machine-learning process includes a sub-score.
 10. The apparatus ofclaim 8, wherein calculating the plurality of sub-scores comprisescalculating each sub-score of the plurality of sub-scores using at leasta compatibility criterion.
 11. The apparatus of claim 1, whereinclassifying the received information comprises using a languageprocessor to match each term of the posting data to a plurality ofsynonyms of terms contained in the action data and user role associatedwith a user identifier of the plurality of user identifiers.
 12. Theapparatus of claim 11, wherein classifying the received informationfurther comprises matching the posting data to a particular useridentifier of the plurality of user identifier as function of theplurality of synonyms.
 13. The apparatus of claim 1, wherein revealingthe user identifier to the posting generator comprises: decrypting theuser identifier stored on the immutable sequential listing; andtransmitting the decrypted user identifier to the posting generator. 14.The apparatus of claim 15, wherein transmitting the decrypted userdevice comprises an electronic notification on a posting generatordevice.
 15. A method for revealing user identifiers on an immutablesequential listing to a posting generator, the method comprising:storing, using a computing device, on an immutable sequential listing, aplurality of user identifiers, wherein: each user identifier of theplurality of user identifiers is associated with the same user; eachuser identifier of the plurality of user identifiers is associated witha plurality of action data; and each user identifier of the plurality ofuser identifiers is associated with a user role of the user; receiving,using the computing device, information relating to an element ofposting data associated with a posting generator; classifying, using thecomputing device, the information to a user identifier of the pluralityof user identifiers as a function of the plurality of action dataassociated with the user identifier, wherein classifying the receivedinformation to the user identifier further comprises: training a machinelearning algorithm with training data to generate a first machinelearning model, wherein the training data correlates posting informationdata to user action data; and classifying the received information tothe user identifier using the first machine learning model; andrevealing, using a computing device, the user identifier to the postinggenerator.
 16. The method of claim 15, wherein classifying the receivedinformation relating to an element of posting data to a user identifierof the plurality of user identifiers further comprises: calculating arelevance score as a function of the plurality of user action data and amachine learning algorithm; and classifying the received information asa function of the relevance score.
 17. The method of claim 16, whereincalculating the relevance score further comprises: using at least amachine learning process to calculate a plurality of sub-scores of anelement of action and user role based on relevance to the posting data;and combining the plurality of sub-scores into the relevance score. 18.The method of claim 15, wherein classifying the received informationcomprises using a language processor to match each term of the postingdata to a plurality of synonyms of terms contained in the action dataand user role associated with a user identifier of the plurality of useridentifiers.
 19. The method of claim 18, wherein classifying thereceived information further comprises matching the posting data to aparticular user identifier of the plurality of user identifier asfunction of the plurality of synonyms.
 20. The method of claim 15,wherein revealing the user identifier to the posting generatorcomprises: decrypting the user identifier stored on the immutablesequential listing; and transmitting the decrypted user identifier tothe posting generator.